Coin-sized, fully integrated, and minimally invasive continuous glucose monitoring system based on organic electrochemical transistors.

Continuous glucose monitoring systems (CGMs) are critical toward closed-loop diabetes management. The field's progress urges next-generation CGMs with enhanced antinoise ability, reliability, and wearability. Here, we propose a coin-sized, fully integrated, and wearable CGM, achieved by holistically synergizing state-of-the-art interdisciplinary technologies of biosensors, minimally invasive tools, and hydrogels. The proposed CGM consists of three major parts: (i) an emerging biochemical signal amplifier, the organic electrochemical transistor (OECT), improving the signal-to-noise ratio (SNR) beyond traditional electrochemical sensors; (ii) a microneedle array to facilitate subcutaneous glucose sampling with minimized pain; and (iii) a soft hydrogel to stabilize the skin-device interface. Compared to conventional CGMs, the OECT-CGM offers a high antinoise ability, tunable sensitivity and resolution, and comfort wearability, enabling personalized glucose sensing for future precision diabetes health care. Last, we discuss how OECT technology can help push the limit of detection of current wearable electrochemical biosensors, especially when operating in complicated conditions.

Association between diabetes mellitus and primary restenosis following endovascular treatment: a comprehensive meta-analysis of randomized controlled trials.

IMPORTANCE: Diabetes mellitus (DM) is thought to be closely related to arterial stenotic or occlusive disease caused by atherosclerosis. However, there is still no definitive clinical evidence to confirm that patients with diabetes have a higher risk of restenosis. OBJECTIVE: This meta-analysis was conducted to determine the effect of DM on restenosis among patients undergoing endovascular treatment, such as percutaneous transluminal angioplasty (PTA) or stenting. DATA SOURCES AND STUDY SELECTION: The PubMed/Medline, EMBASE and Cochrane Library electronic databases were searched from 01/1990 to 12/2022, without language restrictions. Trials were included if they satisfied the following eligibility criteria: (1) RCTs of patients with or without DM; (2) lesions confined to the coronary arteries or femoral popliteal artery; (3) endovascular treatment via PTA or stenting; and (4) an outcome of restenosis at the target lesion site. The exclusion criteria included the following: (1) greater than 20% of patients lost to follow-up and (2) a secondary restenosis operation. DATA EXTRACTION AND SYNTHESIS: Two researchers independently screened the titles and abstracts for relevance, obtained full texts of potentially eligible studies, and assessed suitability based on inclusion and exclusion criteria.. Disagreements were resolved through consultation with a third researcher. Treatment effects were measured by relative ratios (RRs) with 95% confidence intervals (CIs) using random effects models. The quality of the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. MAIN OUTCOMES AND MEASURES: The main observation endpoint was restenosis, including > 50% stenosis at angiography, or TLR of the primary operation lesion during the follow-up period. RESULTS: A total of 31,066 patients from 20 RCTs were included. Patients with DM had a higher risk of primary restenosis after endovascular treatment (RR = 1.43, 95% CI: 1.25-1.62; p = 0.001). CONCLUSIONS AND RELEVANCE: This meta-analysis of all currently available RCTs showed that patients with DM are more prone to primary restenosis after endovascular treatment.

Predictive Role of Platelet and Inflammation Markers for Severe COVID-19 by Propensity Score Matching.

BACKGROUND: This study aims to ascertain the predictive value of platelet and inflammation markers in severe cases of COVID-19. METHODS: A retrospective real-world cohort study was conducted using propensity score matching (PSM). Patients were classified into severe and non-severe COVID-19 groups based on the severity of the disease, and the correlation between severe COVID-19 and laboratory parameters at admission was analyzed. RESULTS: The study included 397 adult patients, comprising 212 (53%) males and 185 (47%) females. Among these, 309 were non-severe and 88 were severe cases. The severe group had a higher median age than the non-severe group (60 vs. 42 years, p < 0.001). Independent risk factors for severe COVID-19 included age, diabetes comorbidity, fever, respiratory symptoms, platelet count, high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and the ratio of arterial oxygen partial pressure (PaO2) to the fraction of inspired oxygen (FiO2) (P/F ratio). After one-to-one PSM, adjusted for age, diabetes comorbidities, fever, and respiratory symptoms, significant differences in laboratory parameters at admission were observed. Compared to the non-severe group (n = 71), in the severe group (n = 71), elevated levels of hsCRP (median: 27.1 mg/L vs. 14.6 mg/L, p = 0.005) and IL-6 (median: 16.2 pg/mL vs. 15.3 pg/mL, p = 0.005) were observed, while platelet count (164 ± 36 × 109 vs. 180 ± 50 × 109, p = 0.02) and P/F ratio (median: 351 vs. 397, p = 0.001) were reduced. CONCLUSIONS: Elevated levels of hsCRP and IL-6, along with reduced platelet count and P/F ratio at admission, were significantly associated with severe COVID-19 and may serve as predictive indicators.

A Combined Measure of the Triglyceride Glucose Index and Trimethylamine N-Oxide in Risk Stratification of ST-Segment Elevation Myocardial Infarction Patients with High-Risk Plaque Features Defined by Optical Coherence Tomography: A Substudy of the OCTAMI Registry Study.

Background and Aim: An elevated triglyceride-glucose (TyG) level is associated with increased risk of mortality in patients with CAD. Trimethylamine N-oxide (TMAO) has mechanistic links to atherosclerotic coronary artery disease (CAD) pathogenesis and is correlated with adverse outcomes. However, the incremental prognostic value of TMAO and TyG in the cohort of optical coherence tomography (OCT)-defined high-risk ST-segment elevation myocardial infarction (STEMI) patients is unknown. Methods: We studied 274 consecutive aged ≥18 years patients with evidence of STEMI and detected on pre-intervention OCT imaging of culprit lesions between March 2017 and March 2019. Outcomes: There were 22 (22.68%), 27 (27.84%), 26 (26.80%), and 22 (22.68%) patients in groups A-D, respectively. The baseline characteristics according to the level of TMAO and TyG showed that patients with higher level in both indicators were more likely to have higher triglycerides (p < 0.001), fasting glucose (p < 0.001) and higher incidence of diabetes (p = 0.008). The group with TMAO > median and TyG ≤ median was associated with higher rates of MACEs significantly (p = 0.009) in fully adjusted analyses. During a median follow-up of 2.027 years, 20 (20.6%) patients experienced MACEs. To evaluate the diagnostic value of the TyG index combined with TMAO, the area under the receiver operating characteristic curve for predicting MACEs after full adjustment was 0.815 (95% confidence interval, 0.723-0.887; sensitivity, 85.00%; specificity, 72.73%; cut-off level, 0.577). Among the group of patients with TMAO > median and TyG ≤ median, there was a significantly higher incidence of MACEs (p=0.033). A similar tendency was found in the cohort with hyperlipidemia (p=0.016) and diabetes mellitus (p=0.036). Conclusion: This study demonstrated the usefulness of combined measures of the TyG index and TMAO in enhancing risk stratification in STEMI patients with OCT-defined high-risk plaque characteristics. Trial Registration: This study was registered at ClinicalTrials.gov as NCT03593928.

In-depth correlation analysis between tear glucose and blood glucose using a wireless smart contact lens.

Tears have emerged as a promising alternative to blood for diagnosing diabetes. Despite increasing attempts to measure tear glucose using smart contact lenses, the controversy surrounding the correlation between tear glucose and blood glucose still limits the clinical usage of tears. Herein, we present an in-depth investigation of the correlation between tear glucose and blood glucose using a wireless and soft smart contact lens for continuous monitoring of tear glucose. This smart contact lens is capable of quantitatively monitoring the tear glucose levels in basal tears excluding the effect of reflex tears which might weaken the relationship with blood glucose. Furthermore, this smart contact lens can provide an unprecedented level of continuous tear glucose data acquisition at sub-minute intervals. These advantages allow the precise estimation of lag time, enabling the establishment of the concept called 'personalized lag time'. This demonstration considers individual differences and is successfully applied to both non-diabetic and diabetic humans, as well as in animal models, resulting in a high correlation.

Identifying a group of factors predicting cognitive impairment among older adults.

BACKGROUND: Cognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011-2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI). RESULTS: Using the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36-12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62-7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race. CONCLUSION: The model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.

Impact of diabetes mellitus on osteoarthritis: a scoping review on biomarkers.

Osteoarthritis (OA) commonly affects the knee and hip joints and accounts for 19.3% of disability-adjusted life years and years lived with disability worldwide (Refs , ). Early management is important in order to avoid disability uphold quality of life (Ref. ). However, a lack of awareness of subclinical and early symptomatic stages of OA often hampers early management (Ref. ). Moreover, late diagnosis of OA among those with severe disease, at a stage when OA management becomes more complicated is common (Refs , , , ). Established risk factors for the development and progression of OA include increasing age, female, history of trauma and obesity (Ref. ). Recent studies have also drawn a link between OA and metabolic syndrome, which is characterized by insulin resistance, dyslipidaemia and hypertension (Refs , ).

A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study.

BACKGROUND: Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes. METHODS AND FINDINGS: We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation. CONCLUSIONS: Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.

A new nomogram to predict in-hospital mortality in patients with acute decompensated chronic heart failure and diabetes after 48 Hours of Intensive Care Unit.

BACKGROUND: The study set out to develop an accurate and clinically valuable prognostic nomogram to assess the risk of in-hospital death in patients with acute decompensated chronic heart failure (ADCHF) and diabetes. METHODS: We extracted clinical data of patients diagnosed with ADCHF and diabetes from the Medical Information Mart for Intensive Care III database. Risk variables were selected utilizing least absolute shrinkage and selection operator regression analysis, and were included in multivariate logistic regression and presented in nomogram. bootstrap was used for internal validation. The discriminative power and predictive accuracy of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). RESULTS: Among 867 patients with ADCHF and diabetes, In-hospital death occurred in 81 (9.3%) patients. Age, heart rate, systolic blood pressure, red blood cell distribution width, shock, ß-blockers, angiotensin converting enzyme inhibitors or angiotensin receptor blockers, assisted ventilation, and blood urea nitrogen were brought into the nomogram model. The calibration curves suggested that the nomogram was well calibrated. The AUC of the nomogram was 0.873 (95% CI: 0.834-0.911), which was higher that of the Simplified Acute Physiology Score II [0.761 (95% CI: 0.711-0.810)] and sequential organ failure assessment score [0.699 (95% CI: 0.642-0.756)], and Guidelines-Heart Failure score [0.782 (95% CI: 0.731-0.835)], indicating that the nomogram had better ability to predict in-hospital mortality. In addition, the internally validated C-index was 0.857 (95% CI: 0.825-0.891), which again verified the validity of this model. CONCLUSIONS: This study constructed a simple and accurate nomogram for predicting in-hospital mortality in patients with ADCHF and diabetes, especially in those who admitted to the intensive care unit for more than 48 hours, which contributed clinicians to assess the risk and individualize the treatment of patients, thereby reducing in-hospital mortality.

Variation in the relationship between fasting glucose and HbA1c: implications for the diagnosis of diabetes in different age and ethnic groups.

INTRODUCTION: Identify non-glycemic factors affecting the relationship between fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c), in order to refine diabetes diagnostic criteria. RESEARCH DESIGN AND METHODS: Relationship between FPG-HbA1c was assessed in 12 531 individuals from 2001 to 2018 US National Health and Nutrition Examination Survey. Using a recently described method, FPG and HbA1c were used to calculate apparent glycation ratio (AGR) of red blood cells for different subgroups based on age, race, and gender. RESULTS: At an FPG of 7 mmol/L, black individuals had a higher HbA1c (p<0.001, mean: 50.2 mmol/mol, 95% CI (49.8 to 50.4)) compared with white individuals (47.4 mmol/mol (47.2 to 47.5)). This corresponds to NGSP (National Glycohemoglobin Standardization Program) units of 6.7% and 6.5% for black versus white individuals, respectively. Similarly, individuals under 21 years had lower HbA1c (p<0.001, 47.9 mmol/mol (47.7 to 48.1), 6.5%) compared with those over 50 years (48.3 mmol/mol (48.2 to 48.5), 6.6%). Differences were also observed between women (p<0.001, 49.2 mmol/mol (49.1 to 49.3), 6.7%) and men (47.0 mmol/mol (46.8 to 47.1), 6.5%). Of note, the difference in HbA1c at FPG of 7 mmol/L in black females over 50 and white males under 21 years was 5 mmol/mol (0.46%). AGR differences according to race (p<0.001), age (p<0.001), and gender (p<0.001) explained altered glucose-HbA1c relationship in the analyzed groups. CONCLUSIONS: FPG-HbA1c relationship is affected by non-glycemic factors leading to incorrect diagnosis of diabetes in some individuals and ethnic groups. Assessment of AGR helps understand individual-specific relationship between glucose levels and HbA1c, which has the potential to more accurately diagnose and manage diabetes.

Relative importance of triglyceride glucose index combined with body mass index in predicting recovery from prediabetic state to normal fasting glucose: a cohort analysis based on a Chinese physical examination population.

BACKGROUND: Prediabetes is a high-risk state for diabetes, and numerous studies have shown that the body mass index (BMI) and triglyceride-glucose (TyG) index play significant roles in risk prediction for blood glucose metabolism. This study aims to evaluate the relative importance of BMI combination with TyG index (TyG-BMI) in predicting the recovery from prediabetic status to normal blood glucose levels. METHODS: A total of 25,397 prediabetic subjects recruited from 32 regions across China. Normal fasting glucose (NFG), prediabetes, and diabetes were defined referring to the American Diabetes Association (ADA) criteria. After normalizing the independent variables, the impact of TyG-BMI on the recovery or progression of prediabetes was analyzed through the Cox regression models. Receiver Operating Characteristic (ROC) curve analysis was utilized to visualize and compare the predictive value of TyG-BMI and its constituent components in prediabetes recovery/progression. RESULTS: During the average observation period of 2.96 years, 10,305 individuals (40.58%) remained in the prediabetic state, 11,278 individuals (44.41%) recovered to NFG, and 3,814 individuals (15.02%) progressed to diabetes. The results of multivariate Cox regression analysis demonstrated that TyG-BMI was negatively associated with recovery from prediabetes to NFG and positively associated with progression from prediabetes to diabetes. Further ROC analysis revealed that TyG-BMI had higher impact and predictive value in predicting prediabetes recovering to NFG or progressing to diabetes in comparison to the TyG index and BMI. Specifically, the TyG-BMI threshold for predicting prediabetes recovery was 214.68, while the threshold for predicting prediabetes progression was 220.27. Additionally, there were significant differences in the relationship of TyG-BMI with prediabetes recovering to NFG or progressing to diabetes within age subgroups. In summary, TyG-BMI is more suitable for assessing prediabetes recovery or progression in younger populations (< 45 years old). CONCLUSIONS: This study, for the first time, has revealed the significant impact and predictive value of the TyG index in combination with BMI on the recovery from prediabetic status to normal blood glucose levels. From the perspective of prediabetes intervention, maintaining TyG-BMI within the threshold of 214.68 holds crucial significance.

Global longitudinal strain and plasma biomarkers for prognosis in heart failure complicated by diabetes: a prospective observational study.

BACKGROUND: Heart failure (HF) and diabetes are associated with increased incidence and worse prognosis of each other. The prognostic value of global longitudinal strain (GLS) measured by cardiovascular magnetic resonance (CMR) has not been established in HF patients with diabetes. METHODS: In this prospective, observational study, consecutive patients (n = 315) with HF underwent CMR at 3T, including GLS, late gadolinium enhancement (LGE), native T1, and extracellular volume fraction (ECV) mapping. Plasma biomarker concentrations were measured including: N-terminal pro B-type natriuretic peptide(NT-proBNP), high-sensitivity troponin T(hs-TnT), growth differentiation factor 15(GDF-15), soluble ST2(sST2), and galectin 3(Gal-3). The primary outcome was a composite of all-cause mortality or HF hospitalisation. RESULTS: Compared to those without diabetes (n = 156), the diabetes group (n = 159) had a higher LGE prevalence (76 vs. 60%, p < 0.05), higher T1 (1285±42 vs. 1269±42ms, p < 0.001), and higher ECV (30.5±3.5 vs. 28.8±4.1%, p < 0.001). The diabetes group had higher NT-pro-BNP, hs-TnT, GDF-15, sST2, and Gal-3. Diabetes conferred worse prognosis (hazard ratio (HR) 2.33 [95% confidence interval (CI) 1.43-3.79], p < 0.001). In multivariable Cox regression analysis including clinical markers and plasma biomarkers, sST2 alone remained independently associated with the primary outcome (HR per 1 ng/mL 1.04 [95% CI 1.02-1.07], p = 0.001). In multivariable Cox regression models in the diabetes group, both GLS and sST2 remained prognostic (GLS: HR 1.12 [95% CI 1.03-1.21], p = 0.01; sST2: HR per 1 ng/mL 1.03 [95% CI 1.00-1.06], p = 0.02). CONCLUSIONS: Compared to HF patients without diabetes, those with diabetes have worse plasma and CMR markers of fibrosis and a more adverse prognosis. GLS by CMR is a powerful and independent prognostic marker in HF patients with diabetes.

Epidemiology of diabetes mellitus in Pakistan: a systematic review protocol.

INTRODUCTION: Diabetes mellitus (DM) is a chronic metabolic disorder characterised by hyperglycaemia resulting from defects in insulin secretion, insulin action or both. As a major global health concern, its prevalence has been steadily increasing. Pakistan, is no exception to this trend, facing a growing burden of non-communicable diseases including DM. This research aims to comprehensively assess the prevalence of DM, and disparities between rural and urban populations as well as between men and women in Pakistan. METHODS AND ANALYSIS: The systematic review will follow Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and will aim to assess DM prevalence in Pakistan. A comprehensive search strategy will be applied to databases like PubMed, Scopus, Cochrane, PakMediNet and CINAHL from inception up to 1st April 2024. We will include studies that focus on diabetes prevalence in the general population, employing WHO or American Diabetes Association criteria for diagnosis of DM. Cross-sectional studies, cohort studies and population-based surveys with a sample size ≥500, in English will be considered. Data extraction will be done as per a predefined proforma which will include study details such as demographics, prevalence data and methodology. A meta-analysis will be performed using a random effect model with an inverse variance weighted method. I2 statistics will be used to examine heterogeneity, and subgroup analyses will be performed. ETHICS AND DISSEMINATION: The findings from the systematic review will be shared by publishing them in a peer-reviewed journal and showcasing them at pertinent conferences. Our analysis will be based on aggregated data and will not involve individual patient information, thus eliminating the need for ethical clearance. PROSPERO REGISTRATION NUMBER: CRD42023453085.

Rural-urban disparities in the diagnosis and treatment of hypertension and diabetes among aging Indians.

INTRODUCTION: Hypertension and diabetes are modifiable risk factors for dementia. We aimed to assess rural-urban disparities in the diagnosis and treatment of these conditions among aging Indians. METHODS: Participants (n = 6316) were from two parallel, prospective aging cohorts in rural and urban India. Using self-report and clinical/biochemical assessments, we subdivided participants with diabetes and hypertension into undiagnosed and untreated groups. Logistic regression and Fairlie decomposition analysis were the statistical methods utilized. RESULTS: There was a significant rural-urban disparity in undiagnosed hypertension (25.14%), untreated hypertension (11.75%), undiagnosed diabetes (16.94%), and untreated diabetes (11.62%). Further, sociodemographic and lifestyle factors, such as age and tobacco use were the common contributors to the disparities in both undiagnosed hypertension and undiagnosed diabetes, whereas education and body mass index (BMI) were significant contributors to the disparity in untreated hypertension. DISCUSSION: Rural Indians face significant healthcare disadvantages as compared to their urban counterparts, which prompts the urgent need for strategies for equitable healthcare.

Determinants of health and mortality in undiagnosed diabetes: A nationally representative US adult, 2011-2020.

AIM: This study examines the determinants of health and mortality associated with undiagnosed diabetes among a nationally representative sample of US adults. METHODS: Data are from the National Health and Nutrition Examination Survey between 2011 and 2012 and 2019-2020. Diabetes status is categorized into three groups: undiagnosed diabetes, diagnosed diabetes, and no diabetes. Multiple logistic regression is used to estimate the association between undiagnosed diabetes and three domains of risk factors, including sociodemographic and health behavioral and clinical factors. Cox proportional hazards models are performed to compare excess mortality risk between the three groups. RESULTS: Young adults, racial minorities, the foreign-born, and individuals with limited access to health care are more likely to be unaware of their diabetes. Moreover, adults without a family history of diabetes and chronic conditions have a higher chance of undiagnosed diabetes. No health behavioral factors are found to be associated with undiagnosed diabetes. Adults with undiagnosed diabetes have a lower risk of all-cause and cardiovascular disease mortality compared to those with diagnosed diabetes, but a higher risk of all-cause mortality than those with no diabetes. CONCLUSION: Targeted public health approaches should address sociodemographic and clinical factors to reduce the burden of undiagnosed diabetes.

Contact Allergy to Allergens in the Swedish Baseline Series Overrepresented in Diabetes Patients with Skin Reactions to Medical Devices - A Retrospective Study from Southern Sweden.

Allergic contact dermatitis is reported among individuals using continuous glucose monitoring systems and insulin pumps. The aim of this study was to describe contact allergy patterns for allergens in the Swedish baseline series and medical device-related allergens among users. Contact allergy to baseline series allergens and isobornyl acrylate was compared between diabetes patients and dermatitis patients patch-tested at the Department of Occupational and Environmental Dermatology during 2017 to 2020. Fifty- four diabetes patients and 2,567 dermatitis patients were included. The prevalence of contact allergy to fragrance mix II and sesquiterpene lactone mix was significantly higher in diabetes patients compared with dermatitis patients. Of the diabetes patients 13.0% and of the dermatitis patients 0.5% tested positive to sesquiterpene lactone mix (p < 0.001). Of the diabetes patients 7.4% and of the dermatitis patients 2.3% tested positive to fragrance mix II (p = 0.041). Of the diabetes patients 70.4% tested positive to medical device-related allergens. Of the diabetes patients 63.0% and of the dermatitis patients 0.2% were allergic to isobornyl acrylate (p < 0.001). In conclusion, not only medical device-related contact allergies, but also contact allergy to baseline series allergens (fragrance mix II and sesquiterpene lactone mix), is overrepresented in diabetes patients who use medical devices.

Interventions for Pancreatitis-New Approaches, Knowledge Gaps, and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

ABSTRACT: There exists no cure for acute, recurrent acute or chronic pancreatitis and treatments to date have been focused on managing symptoms. A recent workshop held by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) focused on interventions that might disrupt or perhaps even reverse the natural course of this heterogenous disease, aiming to identify knowledge gaps and research opportunities that might inform future funding initiatives for NIDDK. The breadth and variety of identified active or planned clinical trials traverses the spectrum of the disease and was conceptually grouped for the workshop into behavioral, nutritional, pharmacologic and biologic, and mechanical interventions. Cognitive and other behavioral therapies are proven interventions for pain and addiction, but barriers exist to their use. Whilst a disease specific instrument quantifying pain is now validated, an equivalent is lacking for nutrition - and both face challenges in ease and frequency of administration. Multiple pharmacologic agents hold promise. Ongoing development of Patient Reported Outcome (PRO) measurements can satisfy Investigative New Drug (IND) regulatory assessments. Despite multiple randomized clinical trials demonstrating benefit, great uncertainty remains regarding patient selection, timing of intervention, and type of mechanical intervention (endoscopic versus surgery). Challenges and opportunities to establish beneficial interventions for patients were identified.